import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE		:     Dict         =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE		:     List[str]         =	{"vocab_file": "spiece.model"}

SCREAMING_SNAKE_CASE		:     str         =	{
    "vocab_file": {
        "bert_for_seq_generation": (
            "https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model"
        ),
    }
}

SCREAMING_SNAKE_CASE		:     Union[str, Any]         =	{"bert_for_seq_generation": 512}

class        UpperCamelCase						(				SCREAMING_SNAKE_CASE_				):
						a__		:Tuple      =			VOCAB_FILES_NAMES
						a__		:List[str]      =			PRETRAINED_VOCAB_FILES_MAP
						a__		:Optional[Any]      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						a__		:Union[str, Any]      =			[]
						a__		:Union[str, Any]      =			['''input_ids''', '''attention_mask''']





						def __init__(self			,     __UpperCamelCase			,     __UpperCamelCase="<s>"			,     __UpperCamelCase="</s>"			,     __UpperCamelCase="<unk>"			,     __UpperCamelCase="<pad>"			,     __UpperCamelCase="<::::>"			,     __UpperCamelCase = None			,     **__UpperCamelCase			,     )				->				None:
												UpperCamelCase_		:							Optional[int]  =		{} if sp_model_kwargs is None else sp_model_kwargs

												# Add extra_ids to the special token list
												super().__init__(
												    bos_token=snake_case__			,     eos_token=snake_case__			,     unk_token=snake_case__			,     pad_token=snake_case__			,     sep_token=snake_case__			,     sp_model_kwargs=self.sp_model_kwargs			,     **snake_case__			,     )

												UpperCamelCase_		:							int  =		vocab_file

												UpperCamelCase_		:							str  =		spm.SentencePieceProcessor(**self.sp_model_kwargs						)
												self.sp_model.Load(snake_case__						)





						@property
						def        A_							(self						)				->				List[str]:
												return self.sp_model.get_piece_size()





						def        A_							(self						)				->				int:
												UpperCamelCase_		:							Optional[Any]  =		{self.convert_ids_to_tokens(snake_case__						): i for i in range(self.vocab_size						)}
												vocab.update(self.added_tokens_encoder						)
												return vocab





						def __getstate__(self						)				->				Any:
												UpperCamelCase_		:							Optional[Any]  =		self.__dict__.copy()
												UpperCamelCase_		:							Dict  =		None
												return state





						def __setstate__(self			,     __UpperCamelCase						)				->				Dict:
												UpperCamelCase_		:							str  =		d

												# for backward compatibility
												if not hasattr(self			,     """sp_model_kwargs"""						):
																		UpperCamelCase_		:							Dict  =		{}

												UpperCamelCase_		:							Union[str, Any]  =		spm.SentencePieceProcessor(**self.sp_model_kwargs						)
												self.sp_model.Load(self.vocab_file						)





						def        A_							(self			,     __UpperCamelCase						)				->				List[str]:
												return self.sp_model.encode(snake_case__			,     out_type=snake_case__						)





						def        A_							(self			,     __UpperCamelCase						)				->				Dict:
												return self.sp_model.piece_to_id(snake_case__						)





						def        A_							(self			,     __UpperCamelCase						)				->				Optional[int]:
												UpperCamelCase_		:							Optional[int]  =		self.sp_model.IdToPiece(snake_case__						)
												return token





						def        A_							(self			,     __UpperCamelCase						)				->				List[str]:
												UpperCamelCase_		:							Any  =		[]
												UpperCamelCase_		:							int  =		""""""
												for token in tokens:
																		# make sure that special tokens are not decoded using sentencepiece model
																		if token in self.all_special_tokens:
																								out_string += self.sp_model.decode(snake_case__						) + token
																								UpperCamelCase_		:							int  =		[]
																		else:
																								current_sub_tokens.append(snake_case__						)
												out_string += self.sp_model.decode(snake_case__						)
												return out_string.strip()





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase = None						)				->				Tuple[str]:
												if not os.path.isdir(snake_case__						):
																		logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''						)
																		return
												UpperCamelCase_		:							List[Any]  =		os.path.join(
												    snake_case__			,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

												if os.path.abspath(self.vocab_file						) != os.path.abspath(snake_case__						) and os.path.isfile(self.vocab_file						):
																		copyfile(self.vocab_file			,     snake_case__						)
												elif not os.path.isfile(self.vocab_file						):
																		with open(snake_case__			,     """wb"""						) as fi:
																								UpperCamelCase_		:							Optional[Any]  =		self.sp_model.serialized_model_proto()
																								fi.write(snake_case__						)

												return (out_vocab_file,)







from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
				A						=			input('Enter image url: ').strip()
				print(F'''Downloading image from {url} ...''')
				A						=			BeautifulSoup(requests.get(url).content, 'html.parser')
				# The image URL is in the content field of the first meta tag with property og:image
				A						=			soup.find('meta', {'property': 'og:image'})['content']
				A						=			requests.get(image_url).content
				A						=			F'''{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg'''
				with open(file_name, 'wb') as fp:
								fp.write(image_data)
				print(F'''Done. Image saved to disk as {file_name}.''')